Neural Computations in Visual Cortex

视觉皮层的神经计算

• 批准号: 8120522
• 负责人: Jonathan D Victor
• 金额: $ 46.71万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8120522
• 关键词: Accounting; Alzheimer' s Disease; Amblyopia; Architecture; Autistic Disorder; Behavior; Brain; Characteristics; Chronic Brain Injury; Complex; Developmental Process; Diagnosis; Entropy; Feedback; Funding; Goals; Image; Individual; Laboratories; Macaca; Measures; Modeling; Morbidity - disease rate; Nature; Neurons; Noise; Pathway interactions; Pattern; Perception; Perceptual disturbance; Play; Population; Process; Recurrence; Research; Role; Shapes; Stimulus; Stroke; Structure; Testing; Texture; V1 neuron; Vision; Visual; Visual Cortex; Visual Perception; Work; area striata; base; cell assembly; cognitive function; design; insight; luminance; meetings; millimeter; network models; public health relevance; relating to nervous system; response; spatiotemporal; statistics; success; tool; visual information

DESCRIPTION (provided by applicant): The goal of this research is to understand the nature of the computations performed by primary visual cortex (V1), and how these calculations are carried out. Even the most basic step in interpreting the visual world -- extracting local features such as lines and edges -- is a difficult computational problem: it must be carried out in the context of cluttered, complex, natural visual scenes; it must be carried out rapidly; and it must be carried out by neural hardware. The generally accepted view is that V1 acts primarily as a feedforward bank of filters, in which feedback and gain controls play a modulatory role. However, models constructed from simple analytically-convenient stimuli provide an incomplete account of responses to natural scenes. Since natural scenes have characteristics that traditional analytic stimuli lack, this observation implies that V1 neurons are sensitive to these distinguishing characteristics, namely, high-order statistics (HOS's). Based on several lines of evidence (including work from the previous funding period and studies in other laboratories), we hypothesize that this sensitivity to HOS's indicates that V1's basic design is that of a strongly recurrent network. In particular, we hypothesize that the characteristics that distinguish a strongly recurrent architecture from a feedforward or modulatory feedback architecture account for V1's ability to extract HOS's. To test these hypotheses, we focus on analyzing V1's responses to stimuli containing HOS's -- because they distinguish among these two contrasting pictures of V1, and because HOS's are precisely the statistical feature that distinguishes natural scenes from traditional analytic stimuli. In Aim 1, we determine the extent of sensitivity of V1 neurons to HOS's, explicitly studying both artificially- constructed stimuli and stimuli derived from natural scenes. In Aim 2, we determine whether dynamic formation of neural assemblies underlies the extraction of HOS's, by analyzing the statistics of multineuronal firing patterns. If successful, this work will provide fundamental insights into the design principles of V1, including how it exploits general features of cortical architecture to carry out the calculations necessary for vision, how sparse representations arise, and the functional significance of cortical neural "noise." PUBLIC HEALTH RELEVANCE: The long-term goal of this project is to understand how the brain analyzes incoming visual information. An enhanced understanding of this process will advance our ability to diagnose and remediate disturbances of perception and cognitive function, which cause significant morbidity in conditions as disparate as amblyopia, autism, Alzheimer's Disease, stroke, and chronic brain injury.

描述（由申请人提供）：本研究的目标是了解初级视觉皮层（V1）执行的计算的性质，以及这些计算是如何进行的。即使是解释视觉世界的最基本步骤——提取线条和边缘等局部特征——也是一个困难的计算问题：它必须在杂乱、复杂、自然的视觉场景的背景下进行；必须迅速进行；并且必须由神经硬件来执行。普遍接受的观点是，V1 主要充当前馈滤波器组，其中反馈和增益控制发挥调制作用。然而，由简单的、便于分析的刺激构建的模型无法完整地描述对自然场景的反应。由于自然场景具有传统分析刺激所缺乏的特征，这一观察结果意味着 V1 神经元对这些区别特征（即高阶统计量（HOS））敏感。基于多条证据（包括之前资助期间的工作和其他实验室的研究），我们假设这种对 HOS 的敏感性表明 V1 的基本设计是强循环网络。特别是，我们假设区分强循环架构与前馈或调制反馈架构的特征解释了 V1 提取 HOS 的能力。为了检验这些假设，我们重点分析 V1 对包含 HOS 的刺激的反应，因为它们区分了 V1 的这两个对比图片，并且因为 HOS 正是区分自然场景与传统分析刺激的统计特​​征。在目标 1 中，我们确定 V1 神经元对 HOS 的敏感程度，明确研究人工构建的刺激和源自自然场景的刺激。在目标 2 中，我们通过分析多神经元放电模式的统计数据来确定神经组件的动态形成是否是 HOS 提取的基础。如果成功，这项工作将为 V1 的设计原理提供基本见解，包括它如何利用皮层结构的一般特征来执行视觉所需的计算、稀疏表示如何产生以及皮层神经“噪声”的功能意义。 公共健康相关性：该项目的长期目标是了解大脑如何分析传入的视觉信息。加深对这一过程的理解将提高我们诊断和治疗感知和认知功能障碍的能力，这些障碍会导致弱视、自闭症、阿尔茨海默病、中风和慢性脑损伤等不同疾病的显着发病率。